def _get_fields_key(resource):
"""Returns the fields key from a resource dict
"""
if resource['code'] in [HTTP_OK, HTTP_ACCEPTED]:
if (MODEL_RE.match(resource_id) or
ANOMALY_RE.match(resource_id)):
return resource['object']['model']['model_fields']
elif CLUSTER_RE.match(resource_id):
return resource['object']['clusters']['fields']
elif CORRELATION_RE.match(resource_id):
return resource['object']['correlations']['fields']
elif STATISTICAL_TEST_RE.match(resource_id):
return resource['object']['statistical_tests']['fields']
elif STATISTICAL_TEST_RE.match(resource_id):
return resource['object']['statistical_tests']['fields']
elif LOGISTIC_REGRESSION_RE.match(resource_id):
return resource['object']['logistic_regression']['fields']
elif ASSOCIATION_RE.match(resource_id):
return resource['object']['associations']['fields']
elif TOPIC_MODEL_RE.match(resource_id):
return resource['object']['topic_model']['fields']
elif SAMPLE_RE.match(resource_id):
return dict([(field['id'], field) for field in
resource['object']['sample']['fields']])
else:
return resource['object']['fields']
return None
def _get_fields_key(resource):
"""Returns the fields key from a resource dict
"""
if resource['code'] in [HTTP_OK, HTTP_ACCEPTED]:
if (MODEL_RE.match(resource_id) or
ANOMALY_RE.match(resource_id)):
return resource['object']['model']['model_fields']
elif CLUSTER_RE.match(resource_id):
return resource['object']['clusters']['fields']
elif CORRELATION_RE.match(resource_id):
return resource['object']['correlations']['fields']
elif STATISTICAL_TEST_RE.match(resource_id):
return resource['object']['statistical_tests']['fields']
elif LOGISTIC_REGRESSION_RE.match(resource_id):
return resource['object']['logistic_regression']['fields']
elif ASSOCIATION_RE.match(resource_id):
return resource['object']['associations']['fields']
elif TOPIC_MODEL_RE.match(resource_id):
return resource['object']['topic_model']['fields']
elif TIME_SERIES_RE.match(resource_id):
return resource['object']['time_series']['fields']
elif DEEPNET_RE.match(resource_id):
return resource['object']['deepnet']['fields']
elif SAMPLE_RE.match(resource_id):
return dict([(field['id'], field) for field in
resource['object']['sample']['fields']])
elif PCA_RE.match(resource_id):
return dict([(field['id'], field) for field in
resource['object']['pca']['fields']])
elif LINEAR_REGRESSION_RE.match(resource_id):
return resource['object']['linear_regression']['fields']
else:
return resource['object']['fields']
return None
def pprint(self, resource, out=sys.stdout):
"""Pretty prints a resource or part of it.
"""
if (isinstance(resource, dict)
and 'object' in resource
and 'resource' in resource):
resource_id = resource['resource']
if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
or MODEL_RE.match(resource_id)
or EVALUATION_RE.match(resource_id)
or ENSEMBLE_RE.match(resource_id)
or CLUSTER_RE.match(resource_id)
or ANOMALY_RE.match(resource_id)
or TOPIC_MODEL_RE.match(resource_id)
or LOGISTIC_REGRESSION_RE.match(resource_id)
or TIME_SERIES_RE.match(resource_id)
or DEEPNET_RE.match(resource_id)
or FUSION_RE.match(resource_id)
or PCA_RE.match(resource_id)
or LINEAR_REGRESSION_RE.match(resource_id)
or OPTIML_RE.match(resource_id)):
out.write("%s (%s bytes)\n" % (resource['object']['name'],
resource['object']['size']))
elif PREDICTION_RE.match(resource['resource']):
objective_field_name = (
resource['object']['fields'][
resource['object']['objective_fields'][0]]['name'])
input_data = {}
for key, value in resource['object']['input_data'].items():
try:
name = resource['object']['fields'][key]['name']
except KeyError:
name = key
input_data[name] = value
prediction = (
resource['object']['prediction'][
resource['object']['objective_fields'][0]])
out.write("%s for %s is %s\n" % (objective_field_name,
input_data,
prediction))
out.flush()
else:
pprint.pprint(resource, out, indent=4)
def pprint(self, resource, out=sys.stdout):
"""Pretty prints a resource or part of it.
"""
if (isinstance(resource, dict)
and 'object' in resource
and 'resource' in resource):
resource_id = resource['resource']
if (SOURCE_RE.match(resource_id) or DATASET_RE.match(resource_id)
or MODEL_RE.match(resource_id)
or EVALUATION_RE.match(resource_id)
or ENSEMBLE_RE.match(resource_id)
or CLUSTER_RE.match(resource_id)
or ANOMALY_RE.match(resource_id)
or TOPIC_MODEL_RE.match(resource_id)
or LOGISTIC_REGRESSION_RE.match(resource_id)
or TIME_SERIES_RE.match(resource_id)
or DEEPNET_RE.match(resource_id)
or FUSION_RE.match(resource_id)
or PCA_RE.match(resource_id)
or LINEAR_REGRESSION_RE.match(resource_id)
or OPTIML_RE.match(resource_id)):
out.write("%s (%s bytes)\n" % (resource['object']['name'],
resource['object']['size']))
elif PREDICTION_RE.match(resource['resource']):
objective_field_name = (
resource['object']['fields'][
resource['object']['objective_fields'][0]]['name'])
input_data = {}
for key, value in resource['object']['input_data'].items():
try:
name = resource['object']['fields'][key]['name']
except KeyError:
name = key
input_data[name] = value
prediction = (
resource['object']['prediction'][
resource['object']['objective_fields'][0]])
out.write("%s for %s is %s\n" % (objective_field_name,
input_data,
prediction))
out.flush()
else:
pprint.pprint(resource, out, indent=4)